{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}D:\jopr_yesilyurt_yesilyurt\yesilyurt_yeslyurt.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 5 May 2019, 20:07:17

{com}. do "D:\jopr_yesilyurt_yesilyurt\yesilyurt_yesilyurt.do"
{txt}
{com}. 
. 
. ***Table I. Descriptive statistics for the three samples 
. use "D:\jopr_yesilyurt_yesilyurt\overallsample.dta", clear
{txt}
{com}.  summarize PCC t Precision Computed_standard_error

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}PCC {c |}{res}        554    .0648339    .3018615      -.844       .868
{txt}{space 11}t {c |}{res}        554    .2517455    2.427569       -5.8       20.6
{txt}{space 3}Precision {c |}{res}        554    10.04863    7.953505   2.291288   46.60876
{txt}Computed_s~r {c |}{res}        554    .1488971    .0798542       .021       .436
{txt}
{com}. use "D:\jopr_yesilyurt_yesilyurt\coresample.dta", clear
{txt}
{com}.  summarize PCC t Precision Computed_standard_error

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}PCC {c |}{res}        235   -.0039702    .3015501      -.844       .679
{txt}{space 11}t {c |}{res}        235    .0725007    2.124806      -5.11     13.379
{txt}{space 3}Precision {c |}{res}        235    8.873046    6.722182   2.291288   34.49072
{txt}Computed_s~r {c |}{res}        235    .1523106    .0691313       .029       .436
{txt}
{com}. use "D:\jopr_yesilyurt_yesilyurt\remainingsample.dta", clear
{txt}
{com}.  summarize PCC t Precision Computed_standard_error

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}PCC {c |}{res}        319    .1155204    .2923517      -.663       .868
{txt}{space 11}t {c |}{res}        319     .383791    2.623853       -5.8       20.6
{txt}{space 3}Precision {c |}{res}        319    10.91466    8.659139   2.468203   46.60876
{txt}Computed_s~r {c |}{res}        319    .1463824    .0869331       .021       .405
{txt}
{com}. 
. 
. ***Table II. Regression of t-statistics on precision: FAT-PET****                                                                                                                                    
. 
. ***CORE Sample with Outlier
. 
. use "D:\jopr_yesilyurt_yesilyurt\coresample.dta", clear
{txt}
{com}. **CORE Sample OLS
. regress t Precision

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       235
{txt}{hline 13}{c +}{hline 34}   F(1, 233)       = {res}     0.67
{txt}       Model {c |} {res}  3.0152441         1   3.0152441   {txt}Prob > F        ={res}    0.4150
{txt}    Residual {c |} {res} 1053.44778       233  4.52123512   {txt}R-squared       ={res}    0.0029
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0014
{txt}       Total {c |} {res} 1056.46303       234  4.51479926   {txt}Root MSE        =   {res} 2.1263

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0168866{col 26}{space 2} .0206781{col 37}{space 1}    0.82{col 46}{space 3}0.415{col 54}{space 4}-.0238533{col 67}{space 3} .0576266
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0773351{col 26}{space 2} .2300073{col 37}{space 1}   -0.34{col 46}{space 3}0.737{col 54}{space 4}-.5304949{col 67}{space 3} .3758246
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *CORE Sample ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       235
                                                {txt}F(1, 233)         =  {res}     0.62
                                                {txt}Prob > F          = {res}    0.4300
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res} 2.1263

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0168866{col 26}{space 2} .0213617{col 37}{space 1}    0.79{col 46}{space 3}0.430{col 54}{space 4}-.0252002{col 67}{space 3} .0589734
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0773351{col 26}{space 2} .2051864{col 37}{space 1}   -0.38{col 46}{space 3}0.707{col 54}{space 4}-.4815929{col 67}{space 3} .3269226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *CORE Sample CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       235
                                                {txt}F(1, 54)          =  {res}     0.30
                                                {txt}Prob > F          = {res}    0.5867
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res} 2.1263

{txt}{ralign 78:(Std. Err. adjusted for {res:55} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0168866{col 26}{space 2} .0308781{col 37}{space 1}    0.55{col 46}{space 3}0.587{col 54}{space 4}-.0450203{col 67}{space 3} .0787935
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0773351{col 26}{space 2} .3266415{col 37}{space 1}   -0.24{col 46}{space 3}0.814{col 54}{space 4} -.732212{col 67}{space 3} .5775417
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. xtmixed t Precision || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-475.78146}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-475.78146}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       235
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        55

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       4.3
{txt}{col 63}max{col 67}={col 69}{res}        28

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.18
{txt}Log likelihood = {res}-475.78146{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.6727

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0103875{col 26}{space 2} .0245887{col 37}{space 1}    0.42{col 46}{space 3}0.673{col 54}{space 4}-.0378055{col 67}{space 3} .0585806
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .096553{col 26}{space 2} .3748628{col 37}{space 1}    0.26{col 46}{space 3}0.797{col 54}{space 4}-.6381647{col 67}{space 3} .8312707
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.116006{col 44} .2718288{col 58} 1.645012{col 70} 2.721855
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.434813{col 44} .0786902{col 58} 1.288583{col 70} 1.597638
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}67.89{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. use "D:\jopr_yesilyurt_yesilyurt\coresample_withoutoutlier.dta", clear
{txt}
{com}. *CORE Sample without Outlier
. regress t Precision 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       232
{txt}{hline 13}{c +}{hline 34}   F(1, 230)       = {res}     0.49
{txt}       Model {c |} {res} 2.24490952         1  2.24490952   {txt}Prob > F        ={res}    0.4842
{txt}    Residual {c |} {res} 1051.46184       230  4.57157321   {txt}R-squared       ={res}    0.0021
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0022
{txt}       Total {c |} {res} 1053.70675       231  4.56150107   {txt}Root MSE        =   {res} 2.1381

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0151519{col 26}{space 2} .0216223{col 37}{space 1}    0.70{col 46}{space 3}0.484{col 54}{space 4}-.0274511{col 67}{space 3}  .057755
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.063089{col 26}{space 2} .2345769{col 37}{space 1}   -0.27{col 46}{space 3}0.788{col 54}{space 4}-.5252833{col 67}{space 3} .3991053
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *CORE Sample ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       232
                                                {txt}F(1, 230)         =  {res}     0.43
                                                {txt}Prob > F          = {res}    0.5108
                                                {txt}R-squared         = {res}    0.0021
                                                {txt}Root MSE          =    {res} 2.1381

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0151519{col 26}{space 2} .0230077{col 37}{space 1}    0.66{col 46}{space 3}0.511{col 54}{space 4}-.0301809{col 67}{space 3} .0604848
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.063089{col 26}{space 2} .2111178{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4}-.4790611{col 67}{space 3} .3528831
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *CORE Sample CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       232
                                                {txt}F(1, 54)          =  {res}     0.21
                                                {txt}Prob > F          = {res}    0.6473
                                                {txt}R-squared         = {res}    0.0021
                                                {txt}Root MSE          =    {res} 2.1381

{txt}{ralign 78:(Std. Err. adjusted for {res:55} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0151519{col 26}{space 2} .0329334{col 37}{space 1}    0.46{col 46}{space 3}0.647{col 54}{space 4}-.0508755{col 67}{space 3} .0811794
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.063089{col 26}{space 2}   .33316{col 37}{space 1}   -0.19{col 46}{space 3}0.851{col 54}{space 4}-.7310345{col 67}{space 3} .6048565
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. 
. xtmixed t Precision || id:, cluster(id)
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-471.27136}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-471.27136}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}       232
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        55

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       4.2
{txt}{col 63}max{col 67}={col 69}{res}        27

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.17
{txt}Log pseudolikelihood = {res}-471.27136{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.6818

{txt}{ralign 78:(Std. Err. adjusted for {res:55} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0112875{col 26}{space 2} .0275308{col 37}{space 1}    0.41{col 46}{space 3}0.682{col 54}{space 4}-.0426718{col 67}{space 3} .0652468
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0790522{col 26}{space 2} .3045825{col 37}{space 1}    0.26{col 46}{space 3}0.795{col 54}{space 4}-.5179185{col 67}{space 3} .6760228
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.110907{col 44} .6215113{col 58} 1.185362{col 70} 3.759128
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.444935{col 44} .1079394{col 58} 1.248136{col 70} 1.672764
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. **Remaining Sample with Outlier
. use "D:\jopr_yesilyurt_yesilyurt\remainingsample.dta", clear
{txt}
{com}. *Remaining Sample  OLS
. regress t Precision

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       319
{txt}{hline 13}{c +}{hline 34}   F(1, 317)       = {res}    43.14
{txt}       Model {c |} {res} 262.250542         1  262.250542   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1927.05324       317   6.0790323   {txt}R-squared       ={res}    0.1198
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1170
{txt}       Total {c |} {res} 2189.30378       318  6.88460309   {txt}Root MSE        =   {res} 2.4656

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.1048745{col 26}{space 2} .0159672{col 37}{space 1}   -6.57{col 46}{space 3}0.000{col 54}{space 4}-.1362896{col 67}{space 3}-.0734594
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.528461{col 26}{space 2} .2223261{col 37}{space 1}    6.87{col 46}{space 3}0.000{col 54}{space 4}  1.09104{col 67}{space 3} 1.965882
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Remaining Sample ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       319
                                                {txt}F(1, 317)         =  {res}    27.14
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1198
                                                {txt}Root MSE          =    {res} 2.4656

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.1048745{col 26}{space 2} .0201327{col 37}{space 1}   -5.21{col 46}{space 3}0.000{col 54}{space 4}-.1444852{col 67}{space 3}-.0652638
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.528461{col 26}{space 2} .1820284{col 37}{space 1}    8.40{col 46}{space 3}0.000{col 54}{space 4} 1.170324{col 67}{space 3} 1.886597
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Remaining Sample CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       319
                                                {txt}F(1, 45)          =  {res}     4.15
                                                {txt}Prob > F          = {res}    0.0475
                                                {txt}R-squared         = {res}    0.1198
                                                {txt}Root MSE          =    {res} 2.4656

{txt}{ralign 78:(Std. Err. adjusted for {res:46} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.1048745{col 26}{space 2}  .051474{col 37}{space 1}   -2.04{col 46}{space 3}0.048{col 54}{space 4}-.2085486{col 67}{space 3}-.0012005
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.528461{col 26}{space 2} .4928326{col 37}{space 1}    3.10{col 46}{space 3}0.003{col 54}{space 4} .5358451{col 67}{space 3} 2.521077
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. 
. xtmixed t Precision || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-640.50994}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-640.50994}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       319
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        46

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.9
{txt}{col 63}max{col 67}={col 69}{res}        58

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.10
{txt}Log likelihood = {res}-640.50994{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.7497

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0049264{col 26}{space 2}  .015442{col 37}{space 1}   -0.32{col 46}{space 3}0.750{col 54}{space 4}-.0351921{col 67}{space 3} .0253393
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .155096{col 26}{space 2} .4046043{col 37}{space 1}    0.38{col 46}{space 3}0.701{col 54}{space 4}-.6379139{col 67}{space 3} .9481059
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.348315{col 44} .2837389{col 58} 1.853142{col 70} 2.975803
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.531665{col 44} .0653593{col 58} 1.408774{col 70} 1.665276
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}198.00{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. 
. *****Remaining Sample without Outlier
. use "D:\jopr_yesilyurt_yesilyurt\remainingsample_withoutoutlier.dta", clear
{txt}
{com}. 
. *Remaining Sample OLS
. regress t Precision 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       310
{txt}{hline 13}{c +}{hline 34}   F(1, 308)       = {res}   120.47
{txt}       Model {c |} {res}    479.855         1     479.855   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1226.80298       308  3.98312655   {txt}R-squared       ={res}    0.2812
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2788
{txt}       Total {c |} {res} 1706.65798       309  5.52316498   {txt}Root MSE        =   {res} 1.9958

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}  -.15166{col 26}{space 2} .0138175{col 37}{space 1}  -10.98{col 46}{space 3}0.000{col 54}{space 4}-.1788486{col 67}{space 3}-.1244715
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.825557{col 26}{space 2} .1830918{col 37}{space 1}    9.97{col 46}{space 3}0.000{col 54}{space 4} 1.465288{col 67}{space 3} 2.185826
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Remaining Sample ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       310
                                                {txt}F(1, 308)         =  {res}   145.89
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2812
                                                {txt}Root MSE          =    {res} 1.9958

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}  -.15166{col 26}{space 2} .0125563{col 37}{space 1}  -12.08{col 46}{space 3}0.000{col 54}{space 4}-.1763669{col 67}{space 3}-.1269531
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.825557{col 26}{space 2} .1524149{col 37}{space 1}   11.98{col 46}{space 3}0.000{col 54}{space 4}  1.52565{col 67}{space 3} 2.125463
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Remaining Sample CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       310
                                                {txt}F(1, 45)          =  {res}    30.45
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2812
                                                {txt}Root MSE          =    {res} 1.9958

{txt}{ralign 78:(Std. Err. adjusted for {res:46} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}  -.15166{col 26}{space 2} .0274838{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.2070152{col 67}{space 3}-.0963048
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.825557{col 26}{space 2} .3953804{col 37}{space 1}    4.62{col 46}{space 3}0.000{col 54}{space 4}  1.02922{col 67}{space 3} 2.621894
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. xtmixed t Precision || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -562.1328}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -562.1328}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       310
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        46

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.7
{txt}{col 63}max{col 67}={col 69}{res}        56

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}    15.27
{txt}Log likelihood = {res} -562.1328{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0001

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0576495{col 26}{space 2}  .014751{col 37}{space 1}   -3.91{col 46}{space 3}0.000{col 54}{space 4} -.086561{col 67}{space 3} -.028738
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5513642{col 26}{space 2}  .345934{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4}-.1266541{col 67}{space 3} 1.229382
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.983767{col 44} .2441683{col 58} 1.558555{col 70} 2.524987
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.250635{col 44} .0545061{col 58}  1.14824{col 70}  1.36216
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}181.91{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. ** Overall Sample with Outlier
. use "D:\jopr_yesilyurt_yesilyurt\overallsample.dta", clear
{txt}
{com}. *Overall Sample OLS
. regress t Precision 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       554
{txt}{hline 13}{c +}{hline 34}   F(1, 552)       = {res}    25.32
{txt}       Model {c |} {res} 142.929999         1  142.929999   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 3115.94914       552   5.6448354   {txt}R-squared       ={res}    0.0439
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0421
{txt}       Total {c |} {res} 3258.87914       553  5.89309067   {txt}Root MSE        =   {res} 2.3759

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0639206{col 26}{space 2} .0127029{col 37}{space 1}   -5.03{col 46}{space 3}0.000{col 54}{space 4}-.0888726{col 67}{space 3}-.0389685
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8940598{col 26}{space 2} .1627362{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .5744019{col 67}{space 3} 1.213718
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Overall Sample  ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       554
                                                {txt}F(1, 552)         =  {res}    14.93
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0439
                                                {txt}Root MSE          =    {res} 2.3759

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0639206{col 26}{space 2} .0165438{col 37}{space 1}   -3.86{col 46}{space 3}0.000{col 54}{space 4}-.0964171{col 67}{space 3} -.031424
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8940598{col 26}{space 2} .1494835{col 37}{space 1}    5.98{col 46}{space 3}0.000{col 54}{space 4} .6004336{col 67}{space 3} 1.187686
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Overall Sample  CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       554
                                                {txt}F(1, 90)          =  {res}     1.91
                                                {txt}Prob > F          = {res}    0.1709
                                                {txt}R-squared         = {res}    0.0439
                                                {txt}Root MSE          =    {res} 2.3759

{txt}{ralign 78:(Std. Err. adjusted for {res:91} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0639206{col 26}{space 2} .0463084{col 37}{space 1}   -1.38{col 46}{space 3}0.171{col 54}{space 4}-.1559203{col 67}{space 3} .0280792
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8940598{col 26}{space 2} .4185159{col 37}{space 1}    2.14{col 46}{space 3}0.035{col 54}{space 4} .0626049{col 67}{space 3} 1.725515
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. xtmixed t Precision || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1119.7391}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1119.7391}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       554
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        91

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.1
{txt}{col 63}max{col 67}={col 69}{res}        58

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     0.08
{txt}Log likelihood = {res}-1119.7391{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.7801

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2} .0036768{col 26}{space 2} .0131709{col 37}{space 1}    0.28{col 46}{space 3}0.780{col 54}{space 4}-.0221378{col 67}{space 3} .0294913
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1152493{col 26}{space 2} .2850628{col 37}{space 1}    0.40{col 46}{space 3}0.686{col 54}{space 4}-.4434634{col 67}{space 3} .6739621
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 2.276135{col 44} .2066133{col 58} 1.905159{col 70} 2.719348
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.522757{col 44}  .050455{col 58}  1.42701{col 70} 1.624929
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}289.53{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. 
. ** Overall Sample without Outlier
. use "D:\jopr_yesilyurt_yesilyurt\overallsample_withoutoutlier.dta", clear
{txt}
{com}. *Overall Sample OLS
. regress t Precision

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       547
{txt}{hline 13}{c +}{hline 34}   F(1, 545)       = {res}    47.78
{txt}       Model {c |} {res} 195.176649         1  195.176649   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 2226.29093       545  4.08493748   {txt}R-squared       ={res}    0.0806
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0789
{txt}       Total {c |} {res} 2421.46758       546   4.4349223   {txt}Root MSE        =   {res} 2.0211

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0759763{col 26}{space 2} .0109915{col 37}{space 1}   -6.91{col 46}{space 3}0.000{col 54}{space 4}-.0975672{col 67}{space 3}-.0543854
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9435952{col 26}{space 2} .1389093{col 37}{space 1}    6.79{col 46}{space 3}0.000{col 54}{space 4} .6707321{col 67}{space 3} 1.216458
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Overall Sample ROBUST SE
. regress t Precision, vce(robust)

{txt}Linear regression                               Number of obs     = {res}       547
                                                {txt}F(1, 545)         =  {res}    33.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0806
                                                {txt}Root MSE          =    {res} 2.0211

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0759763{col 26}{space 2} .0130951{col 37}{space 1}   -5.80{col 46}{space 3}0.000{col 54}{space 4}-.1016994{col 67}{space 3}-.0502532
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9435952{col 26}{space 2} .1390467{col 37}{space 1}    6.79{col 46}{space 3}0.000{col 54}{space 4} .6704621{col 67}{space 3} 1.216728
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *Overall Sample CLUSTER
. regress t Precision, cluster(id)

{txt}Linear regression                               Number of obs     = {res}       547
                                                {txt}F(1, 89)          =  {res}     2.90
                                                {txt}Prob > F          = {res}    0.0921
                                                {txt}R-squared         = {res}    0.0806
                                                {txt}Root MSE          =    {res} 2.0211

{txt}{ralign 78:(Std. Err. adjusted for {res:90} clusters in id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0759763{col 26}{space 2} .0446157{col 37}{space 1}   -1.70{col 46}{space 3}0.092{col 54}{space 4}-.1646268{col 67}{space 3} .0126741
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9435952{col 26}{space 2} .4072813{col 37}{space 1}    2.32{col 46}{space 3}0.023{col 54}{space 4} .1343359{col 67}{space 3} 1.752855
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ***Table II.I Regression of t-statistics on precision: FAT-PET with multi-level hierarchical analysis 
. xtmixed t Precision|| id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1021.4244}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1021.4244}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       547
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        90

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.1
{txt}{col 63}max{col 67}={col 69}{res}        58

{col 49}{txt}Wald chi2({res}1{txt}){col 67}={col 70}{res}     1.44
{txt}Log likelihood = {res}-1021.4244{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.2305

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Precision {c |}{col 14}{res}{space 2}-.0137545{col 26}{space 2} .0114718{col 37}{space 1}   -1.20{col 46}{space 3}0.231{col 54}{space 4}-.0362389{col 67}{space 3} .0087299
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0733677{col 26}{space 2} .2215208{col 37}{space 1}    0.33{col 46}{space 3}0.740{col 54}{space 4} -.360805{col 67}{space 3} .5075404
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.669652{col 44} .1583652{col 58} 1.386405{col 70} 2.010767
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.338726{col 44} .0444625{col 58} 1.254357{col 70}  1.42877
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}277.26{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. ***Table_III._Regression_of_t-statistics_on_precision_and_controls_(divided_by_standard_errors)_for_the_three_samples_with_t-statistics_based_on_clustered_standard_errors
. 
. **CORE Sample 
. 
. *CORE Sample CLUSTER
. use "D:\jopr_yesilyurt_yesilyurt\coresample.dta", clear
{txt}
{com}. regress t Precision Post_cold_war_dummy Cross_sectional_data_dummy Dynamic_model_dummy ACDA_NIPA_data_dummy SIPRI_data_dummy North_America_Countries_dummy Europe_Countries_dummy Asia_Countries_dummy OLS_dummy Threshold_dummy Twosls_dummy Dummy80 GDP_dummy Investment_dummy Unemployment_dummy Balance_of_payments_dummy Interest_rate_dummy Government_revenue_dummy Debt_dummy Trend_dummy Industry_dummy World_indicator_dummy,cluster(id)

{txt}Linear regression                               Number of obs     = {res}       235
                                                {txt}{help j_robustsingular:{txt}F(21, 54) }        =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.5896
                                                {txt}Root MSE          =    {res} 1.4334

{txt}{ralign 95:(Std. Err. adjusted for {res:55} clusters in id)}
{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                            t{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      t{col 63}   P>|t|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}Precision {c |}{col 31}{res}{space 2} .5056074{col 43}{space 2} .0548095{col 54}{space 1}    9.22{col 63}{space 3}0.000{col 71}{space 4} .3957209{col 84}{space 3} .6154939
{txt}{space 10}Post_cold_war_dummy {c |}{col 31}{res}{space 2}-.3324935{col 43}{space 2} .0380566{col 54}{space 1}   -8.74{col 63}{space 3}0.000{col 71}{space 4}-.4087923{col 84}{space 3}-.2561947
{txt}{space 3}Cross_sectional_data_dummy {c |}{col 31}{res}{space 2} .1262062{col 43}{space 2} .0255513{col 54}{space 1}    4.94{col 63}{space 3}0.000{col 71}{space 4} .0749788{col 84}{space 3} .1774336
{txt}{space 10}Dynamic_model_dummy {c |}{col 31}{res}{space 2}-.2557455{col 43}{space 2} .0371158{col 54}{space 1}   -6.89{col 63}{space 3}0.000{col 71}{space 4}-.3301582{col 84}{space 3}-.1813329
{txt}{space 9}ACDA_NIPA_data_dummy {c |}{col 31}{res}{space 2}-.1428643{col 43}{space 2} .0438959{col 54}{space 1}   -3.25{col 63}{space 3}0.002{col 71}{space 4}-.2308703{col 84}{space 3}-.0548582
{txt}{space 13}SIPRI_data_dummy {c |}{col 31}{res}{space 2}-.0772771{col 43}{space 2}  .031675{col 54}{space 1}   -2.44{col 63}{space 3}0.018{col 71}{space 4}-.1407816{col 84}{space 3}-.0137726
{txt}North_America_Countries_dummy {c |}{col 31}{res}{space 2} .7342599{col 43}{space 2} .0545665{col 54}{space 1}   13.46{col 63}{space 3}0.000{col 71}{space 4} .6248606{col 84}{space 3} .8436591
{txt}{space 7}Europe_Countries_dummy {c |}{col 31}{res}{space 2}-.1834505{col 43}{space 2} .0456289{col 54}{space 1}   -4.02{col 63}{space 3}0.000{col 71}{space 4}-.2749309{col 84}{space 3}-.0919701
{txt}{space 9}Asia_Countries_dummy {c |}{col 31}{res}{space 2} -.185215{col 43}{space 2} .0790064{col 54}{space 1}   -2.34{col 63}{space 3}0.023{col 71}{space 4}-.3436132{col 84}{space 3}-.0268168
{txt}{space 20}OLS_dummy {c |}{col 31}{res}{space 2} -.106425{col 43}{space 2} .0198996{col 54}{space 1}   -5.35{col 63}{space 3}0.000{col 71}{space 4}-.1463214{col 84}{space 3}-.0665287
{txt}{space 14}Threshold_dummy {c |}{col 31}{res}{space 2}-.3552169{col 43}{space 2} .0489354{col 54}{space 1}   -7.26{col 63}{space 3}0.000{col 71}{space 4}-.4533266{col 84}{space 3}-.2571073
{txt}{space 17}Twosls_dummy {c |}{col 31}{res}{space 2}-.0667766{col 43}{space 2} .0227765{col 54}{space 1}   -2.93{col 63}{space 3}0.005{col 71}{space 4}-.1124407{col 84}{space 3}-.0211125
{txt}{space 22}Dummy80 {c |}{col 31}{res}{space 2}-.0811716{col 43}{space 2} .0287936{col 54}{space 1}   -2.82{col 63}{space 3}0.007{col 71}{space 4}-.1388993{col 84}{space 3}-.0234438
{txt}{space 20}GDP_dummy {c |}{col 31}{res}{space 2} -.082349{col 43}{space 2} .0370288{col 54}{space 1}   -2.22{col 63}{space 3}0.030{col 71}{space 4}-.1565873{col 84}{space 3}-.0081107
{txt}{space 13}Investment_dummy {c |}{col 31}{res}{space 2}-.0706457{col 43}{space 2} .0220368{col 54}{space 1}   -3.21{col 63}{space 3}0.002{col 71}{space 4}-.1148269{col 84}{space 3}-.0264646
{txt}{space 11}Unemployment_dummy {c |}{col 31}{res}{space 2} -.494878{col 43}{space 2}   .06827{col 54}{space 1}   -7.25{col 63}{space 3}0.000{col 71}{space 4}-.6317511{col 84}{space 3}-.3580049
{txt}{space 4}Balance_of_payments_dummy {c |}{col 31}{res}{space 2}-.2145732{col 43}{space 2} .0748025{col 54}{space 1}   -2.87{col 63}{space 3}0.006{col 71}{space 4}-.3645432{col 84}{space 3}-.0646033
{txt}{space 10}Interest_rate_dummy {c |}{col 31}{res}{space 2} .3862217{col 43}{space 2} .0374717{col 54}{space 1}   10.31{col 63}{space 3}0.000{col 71}{space 4} .3110954{col 84}{space 3}  .461348
{txt}{space 5}Government_revenue_dummy {c |}{col 31}{res}{space 2} .2705337{col 43}{space 2} .0861345{col 54}{space 1}    3.14{col 63}{space 3}0.003{col 71}{space 4} .0978444{col 84}{space 3}  .443223
{txt}{space 19}Debt_dummy {c |}{col 31}{res}{space 2} .3379582{col 43}{space 2} .0696472{col 54}{space 1}    4.85{col 63}{space 3}0.000{col 71}{space 4} .1983239{col 84}{space 3} .4775924
{txt}{space 18}Trend_dummy {c |}{col 31}{res}{space 2} -.223871{col 43}{space 2} .0556849{col 54}{space 1}   -4.02{col 63}{space 3}0.000{col 71}{space 4}-.3355124{col 84}{space 3}-.1122295
{txt}{space 15}Industry_dummy {c |}{col 31}{res}{space 2} .3200914{col 43}{space 2} .0302697{col 54}{space 1}   10.57{col 63}{space 3}0.000{col 71}{space 4} .2594043{col 84}{space 3} .3807785
{txt}{space 8}World_indicator_dummy {c |}{col 31}{res}{space 2}-.2205434{col 43}{space 2} .0363309{col 54}{space 1}   -6.07{col 63}{space 3}0.000{col 71}{space 4}-.2933824{col 84}{space 3}-.1477044
{txt}{space 24}_cons {c |}{col 31}{res}{space 2}-1.213936{col 43}{space 2} .2715237{col 54}{space 1}   -4.47{col 63}{space 3}0.000{col 71}{space 4}-1.758308{col 84}{space 3}-.6695635
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *CORE Sample hierarchical ols
. xtmixed t Precision Post_cold_war_dummy Cross_sectional_data_dummy Dynamic_model_dummy ACDA_NIPA_data_dummy SIPRI_data_dummy North_America_Countries_dummy Europe_Countries_dummy Asia_Countries_dummy OLS_dummy Threshold_dummy Twosls_dummy Dummy80 GDP_dummy Investment_dummy Unemployment_dummy Balance_of_payments_dummy Interest_rate_dummy Government_revenue_dummy Debt_dummy Trend_dummy Industry_dummy World_indicator_dummy || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-405.31019}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-405.07682}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-405.07614}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-405.07614}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       235
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        55

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       4.3
{txt}{col 63}max{col 67}={col 69}{res}        28

{col 49}{txt}Wald chi2({res}23{txt}){col 67}={col 70}{res}   309.29
{txt}Log likelihood = {res}-405.07614{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            t{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      z{col 63}   P>|z|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}Precision {c |}{col 31}{res}{space 2} .4985728{col 43}{space 2} .0566137{col 54}{space 1}    8.81{col 63}{space 3}0.000{col 71}{space 4}  .387612{col 84}{space 3} .6095336
{txt}{space 10}Post_cold_war_dummy {c |}{col 31}{res}{space 2}-.3395245{col 43}{space 2} .0495243{col 54}{space 1}   -6.86{col 63}{space 3}0.000{col 71}{space 4}-.4365904{col 84}{space 3}-.2424585
{txt}{space 3}Cross_sectional_data_dummy {c |}{col 31}{res}{space 2} .1178917{col 43}{space 2} .0344618{col 54}{space 1}    3.42{col 63}{space 3}0.001{col 71}{space 4} .0503479{col 84}{space 3} .1854355
{txt}{space 10}Dynamic_model_dummy {c |}{col 31}{res}{space 2}-.2541132{col 43}{space 2} .0410472{col 54}{space 1}   -6.19{col 63}{space 3}0.000{col 71}{space 4}-.3345643{col 84}{space 3}-.1736622
{txt}{space 9}ACDA_NIPA_data_dummy {c |}{col 31}{res}{space 2}-.1273104{col 43}{space 2} .0526199{col 54}{space 1}   -2.42{col 63}{space 3}0.016{col 71}{space 4}-.2304434{col 84}{space 3}-.0241774
{txt}{space 13}SIPRI_data_dummy {c |}{col 31}{res}{space 2}-.0695315{col 43}{space 2} .0353828{col 54}{space 1}   -1.97{col 63}{space 3}0.049{col 71}{space 4}-.1388805{col 84}{space 3}-.0001824
{txt}North_America_Countries_dummy {c |}{col 31}{res}{space 2} .7283789{col 43}{space 2} .1299423{col 54}{space 1}    5.61{col 63}{space 3}0.000{col 71}{space 4} .4736967{col 84}{space 3} .9830611
{txt}{space 7}Europe_Countries_dummy {c |}{col 31}{res}{space 2}-.1880606{col 43}{space 2}  .044386{col 54}{space 1}   -4.24{col 63}{space 3}0.000{col 71}{space 4}-.2750556{col 84}{space 3}-.1010656
{txt}{space 9}Asia_Countries_dummy {c |}{col 31}{res}{space 2}-.1905072{col 43}{space 2} .0530977{col 54}{space 1}   -3.59{col 63}{space 3}0.000{col 71}{space 4}-.2945767{col 84}{space 3}-.0864376
{txt}{space 20}OLS_dummy {c |}{col 31}{res}{space 2}-.1104699{col 43}{space 2} .0226415{col 54}{space 1}   -4.88{col 63}{space 3}0.000{col 71}{space 4}-.1548465{col 84}{space 3}-.0660934
{txt}{space 14}Threshold_dummy {c |}{col 31}{res}{space 2}-.3526585{col 43}{space 2} .0755092{col 54}{space 1}   -4.67{col 63}{space 3}0.000{col 71}{space 4}-.5006538{col 84}{space 3}-.2046632
{txt}{space 17}Twosls_dummy {c |}{col 31}{res}{space 2}-.0674012{col 43}{space 2} .0348154{col 54}{space 1}   -1.94{col 63}{space 3}0.053{col 71}{space 4}-.1356381{col 84}{space 3} .0008358
{txt}{space 22}Dummy80 {c |}{col 31}{res}{space 2}-.0813788{col 43}{space 2} .0423788{col 54}{space 1}   -1.92{col 63}{space 3}0.055{col 71}{space 4}-.1644396{col 84}{space 3} .0016821
{txt}{space 20}GDP_dummy {c |}{col 31}{res}{space 2}-.0667153{col 43}{space 2} .0349879{col 54}{space 1}   -1.91{col 63}{space 3}0.057{col 71}{space 4}-.1352903{col 84}{space 3} .0018597
{txt}{space 13}Investment_dummy {c |}{col 31}{res}{space 2}-.0771067{col 43}{space 2}  .026739{col 54}{space 1}   -2.88{col 63}{space 3}0.004{col 71}{space 4}-.1295142{col 84}{space 3}-.0246992
{txt}{space 11}Unemployment_dummy {c |}{col 31}{res}{space 2}-.5140204{col 43}{space 2} .1608503{col 54}{space 1}   -3.20{col 63}{space 3}0.001{col 71}{space 4}-.8292811{col 84}{space 3}-.1987596
{txt}{space 4}Balance_of_payments_dummy {c |}{col 31}{res}{space 2} -.213285{col 43}{space 2} .0699823{col 54}{space 1}   -3.05{col 63}{space 3}0.002{col 71}{space 4}-.3504478{col 84}{space 3}-.0761222
{txt}{space 10}Interest_rate_dummy {c |}{col 31}{res}{space 2} .3798836{col 43}{space 2} .0830907{col 54}{space 1}    4.57{col 63}{space 3}0.000{col 71}{space 4} .2170288{col 84}{space 3} .5427383
{txt}{space 5}Government_revenue_dummy {c |}{col 31}{res}{space 2} .2440325{col 43}{space 2} .1388497{col 54}{space 1}    1.76{col 63}{space 3}0.079{col 71}{space 4}-.0281079{col 84}{space 3}  .516173
{txt}{space 19}Debt_dummy {c |}{col 31}{res}{space 2} .3363869{col 43}{space 2} .0741127{col 54}{space 1}    4.54{col 63}{space 3}0.000{col 71}{space 4} .1911287{col 84}{space 3} .4816451
{txt}{space 18}Trend_dummy {c |}{col 31}{res}{space 2}-.2315247{col 43}{space 2} .0497073{col 54}{space 1}   -4.66{col 63}{space 3}0.000{col 71}{space 4}-.3289492{col 84}{space 3}-.1341002
{txt}{space 15}Industry_dummy {c |}{col 31}{res}{space 2} .3171999{col 43}{space 2} .0663713{col 54}{space 1}    4.78{col 63}{space 3}0.000{col 71}{space 4} .1871144{col 84}{space 3} .4472853
{txt}{space 8}World_indicator_dummy {c |}{col 31}{res}{space 2}-.2252822{col 43}{space 2} .0712931{col 54}{space 1}   -3.16{col 63}{space 3}0.002{col 71}{space 4} -.365014{col 84}{space 3}-.0855504
{txt}{space 24}_cons {c |}{col 31}{res}{space 2}-1.151298{col 43}{space 2} .2466653{col 54}{space 1}   -4.67{col 63}{space 3}0.000{col 71}{space 4}-1.634753{col 84}{space 3}-.6678424
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .3116522{col 44} .2108187{col 58} .0827696{col 70} 1.173464
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.325028{col 44} .0717933{col 58} 1.191529{col 70} 1.473483
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}0.67{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.2064
{txt}
{com}. 
. 
. **REMAINING Sample
. use "D:\jopr_yesilyurt_yesilyurt\remainingsample.dta", clear
{txt}
{com}. *REMAINING Sample cluster 
. regress t Precision Intime_period Cold_War_dummy Time_series_data_dummy Dynamic_model_dummy Unit_Root_dummy Heteroskedasticity_test_dummy Descriptive_statistics_dummy ACDA_NIPA_data_dummy Mixed_Countries_dummy GLS_dummy Dummy70 Dummy80 GDP_dummy Investment_dummy Labour_dummy Inflows_dummy Balance_of_payments_dummy Debt_dummy World_indicator_dummy,cluster(id)

{txt}Linear regression                               Number of obs     = {res}       319
                                                {txt}F(20, 45)         =  {res} 10088.70
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.7632
                                                {txt}Root MSE          =    {res}  1.319

{txt}{ralign 95:(Std. Err. adjusted for {res:46} clusters in id)}
{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                            t{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      t{col 63}   P>|t|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}Precision {c |}{col 31}{res}{space 2} .2781552{col 43}{space 2} .0590893{col 54}{space 1}    4.71{col 63}{space 3}0.000{col 71}{space 4} .1591433{col 84}{space 3} .3971672
{txt}{space 16}Intime_period {c |}{col 31}{res}{space 2}-.6635285{col 43}{space 2}  .194719{col 54}{space 1}   -3.41{col 63}{space 3}0.001{col 71}{space 4}-1.055713{col 84}{space 3}-.2713443
{txt}{space 15}Cold_War_dummy {c |}{col 31}{res}{space 2} .0930852{col 43}{space 2} .0417088{col 54}{space 1}    2.23{col 63}{space 3}0.031{col 71}{space 4} .0090793{col 84}{space 3} .1770912
{txt}{space 7}Time_series_data_dummy {c |}{col 31}{res}{space 2} .4247825{col 43}{space 2} .1540019{col 54}{space 1}    2.76{col 63}{space 3}0.008{col 71}{space 4} .1146069{col 84}{space 3} .7349582
{txt}{space 10}Dynamic_model_dummy {c |}{col 31}{res}{space 2}-.3839546{col 43}{space 2} .0289081{col 54}{space 1}  -13.28{col 63}{space 3}0.000{col 71}{space 4}-.4421784{col 84}{space 3}-.3257307
{txt}{space 14}Unit_Root_dummy {c |}{col 31}{res}{space 2} .1019873{col 43}{space 2} .0496151{col 54}{space 1}    2.06{col 63}{space 3}0.046{col 71}{space 4} .0020574{col 84}{space 3} .2019172
{txt}Heteroskedasticity_test_dummy {c |}{col 31}{res}{space 2} .2338954{col 43}{space 2} .0509477{col 54}{space 1}    4.59{col 63}{space 3}0.000{col 71}{space 4} .1312815{col 84}{space 3} .3365093
{txt}{space 1}Descriptive_statistics_dummy {c |}{col 31}{res}{space 2}  .208352{col 43}{space 2} .0227445{col 54}{space 1}    9.16{col 63}{space 3}0.000{col 71}{space 4} .1625421{col 84}{space 3} .2541619
{txt}{space 9}ACDA_NIPA_data_dummy {c |}{col 31}{res}{space 2}-.1011588{col 43}{space 2} .0434023{col 54}{space 1}   -2.33{col 63}{space 3}0.024{col 71}{space 4}-.1885755{col 84}{space 3}-.0137421
{txt}{space 8}Mixed_Countries_dummy {c |}{col 31}{res}{space 2}-.0881959{col 43}{space 2} .0186443{col 54}{space 1}   -4.73{col 63}{space 3}0.000{col 71}{space 4}-.1257474{col 84}{space 3}-.0506443
{txt}{space 20}GLS_dummy {c |}{col 31}{res}{space 2}-.1047873{col 43}{space 2} .0346059{col 54}{space 1}   -3.03{col 63}{space 3}0.004{col 71}{space 4}-.1744872{col 84}{space 3}-.0350873
{txt}{space 22}Dummy70 {c |}{col 31}{res}{space 2}-.1757393{col 43}{space 2} .0197427{col 54}{space 1}   -8.90{col 63}{space 3}0.000{col 71}{space 4}-.2155031{col 84}{space 3}-.1359755
{txt}{space 22}Dummy80 {c |}{col 31}{res}{space 2} .1183406{col 43}{space 2} .0320974{col 54}{space 1}    3.69{col 63}{space 3}0.001{col 71}{space 4} .0536931{col 84}{space 3} .1829881
{txt}{space 20}GDP_dummy {c |}{col 31}{res}{space 2}-.1354935{col 43}{space 2} .0195903{col 54}{space 1}   -6.92{col 63}{space 3}0.000{col 71}{space 4}-.1749504{col 84}{space 3}-.0960365
{txt}{space 13}Investment_dummy {c |}{col 31}{res}{space 2}-.2579745{col 43}{space 2} .0338964{col 54}{space 1}   -7.61{col 63}{space 3}0.000{col 71}{space 4}-.3262454{col 84}{space 3}-.1897037
{txt}{space 17}Labour_dummy {c |}{col 31}{res}{space 2} .1003167{col 43}{space 2} .0488857{col 54}{space 1}    2.05{col 63}{space 3}0.046{col 71}{space 4} .0018558{col 84}{space 3} .1987775
{txt}{space 16}Inflows_dummy {c |}{col 31}{res}{space 2}-.3391742{col 43}{space 2} .1222485{col 54}{space 1}   -2.77{col 63}{space 3}0.008{col 71}{space 4}-.5853953{col 84}{space 3}-.0929531
{txt}{space 4}Balance_of_payments_dummy {c |}{col 31}{res}{space 2}-.2320267{col 43}{space 2} .1205854{col 54}{space 1}   -1.92{col 63}{space 3}0.061{col 71}{space 4}-.4748982{col 84}{space 3} .0108448
{txt}{space 19}Debt_dummy {c |}{col 31}{res}{space 2} .3272329{col 43}{space 2} .0890338{col 54}{space 1}    3.68{col 63}{space 3}0.001{col 71}{space 4} .1479097{col 84}{space 3} .5065561
{txt}{space 8}World_indicator_dummy {c |}{col 31}{res}{space 2} -.279573{col 43}{space 2} .0556326{col 54}{space 1}   -5.03{col 63}{space 3}0.000{col 71}{space 4}-.3916228{col 84}{space 3}-.1675231
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 1.210966{col 43}{space 2} .2661641{col 54}{space 1}    4.55{col 63}{space 3}0.000{col 71}{space 4} .6748843{col 84}{space 3} 1.747048
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *REMAINING Sample hierarchical ols
. xtmixed t Precision Intime_period Cold_War_dummy Time_series_data_dummy Dynamic_model_dummy Unit_Root_dummy Heteroskedasticity_test_dummy Descriptive_statistics_dummy ACDA_NIPA_data_dummy Mixed_Countries_dummy GLS_dummy Dummy70 Dummy80 GDP_dummy Investment_dummy Labour_dummy Inflows_dummy Balance_of_payments_dummy Debt_dummy World_indicator_dummy || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-521.06252}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-521.05018}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-521.05013}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-521.05013}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       319
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        46

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.9
{txt}{col 63}max{col 67}={col 69}{res}        58

{col 49}{txt}Wald chi2({res}20{txt}){col 67}={col 70}{res}   491.88
{txt}Log likelihood = {res}-521.05013{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            t{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      z{col 63}   P>|z|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}Precision {c |}{col 31}{res}{space 2} .3049205{col 43}{space 2} .0413409{col 54}{space 1}    7.38{col 63}{space 3}0.000{col 71}{space 4} .2238938{col 84}{space 3} .3859472
{txt}{space 16}Intime_period {c |}{col 31}{res}{space 2}-.7429644{col 43}{space 2} .1157317{col 54}{space 1}   -6.42{col 63}{space 3}0.000{col 71}{space 4}-.9697943{col 84}{space 3}-.5161345
{txt}{space 15}Cold_War_dummy {c |}{col 31}{res}{space 2} .0564635{col 43}{space 2} .0335855{col 54}{space 1}    1.68{col 63}{space 3}0.093{col 71}{space 4}-.0093629{col 84}{space 3} .1222899
{txt}{space 7}Time_series_data_dummy {c |}{col 31}{res}{space 2} .4250344{col 43}{space 2} .0723297{col 54}{space 1}    5.88{col 63}{space 3}0.000{col 71}{space 4} .2832707{col 84}{space 3}  .566798
{txt}{space 10}Dynamic_model_dummy {c |}{col 31}{res}{space 2}-.4156995{col 43}{space 2} .0314688{col 54}{space 1}  -13.21{col 63}{space 3}0.000{col 71}{space 4}-.4773772{col 84}{space 3}-.3540218
{txt}{space 14}Unit_Root_dummy {c |}{col 31}{res}{space 2} .0380188{col 43}{space 2} .0462653{col 54}{space 1}    0.82{col 63}{space 3}0.411{col 71}{space 4}-.0526595{col 84}{space 3} .1286972
{txt}Heteroskedasticity_test_dummy {c |}{col 31}{res}{space 2} .2445795{col 43}{space 2} .0353013{col 54}{space 1}    6.93{col 63}{space 3}0.000{col 71}{space 4} .1753904{col 84}{space 3} .3137687
{txt}{space 1}Descriptive_statistics_dummy {c |}{col 31}{res}{space 2} .2135456{col 43}{space 2}  .030175{col 54}{space 1}    7.08{col 63}{space 3}0.000{col 71}{space 4} .1544037{col 84}{space 3} .2726876
{txt}{space 9}ACDA_NIPA_data_dummy {c |}{col 31}{res}{space 2} -.057591{col 43}{space 2} .0525533{col 54}{space 1}   -1.10{col 63}{space 3}0.273{col 71}{space 4}-.1605936{col 84}{space 3} .0454115
{txt}{space 8}Mixed_Countries_dummy {c |}{col 31}{res}{space 2}-.0974866{col 43}{space 2} .0204176{col 54}{space 1}   -4.77{col 63}{space 3}0.000{col 71}{space 4}-.1375044{col 84}{space 3}-.0574689
{txt}{space 20}GLS_dummy {c |}{col 31}{res}{space 2}-.1034445{col 43}{space 2} .0300548{col 54}{space 1}   -3.44{col 63}{space 3}0.001{col 71}{space 4}-.1623508{col 84}{space 3}-.0445382
{txt}{space 22}Dummy70 {c |}{col 31}{res}{space 2} -.175573{col 43}{space 2} .0303342{col 54}{space 1}   -5.79{col 63}{space 3}0.000{col 71}{space 4}-.2350269{col 84}{space 3}-.1161191
{txt}{space 22}Dummy80 {c |}{col 31}{res}{space 2} .1313675{col 43}{space 2} .0427444{col 54}{space 1}    3.07{col 63}{space 3}0.002{col 71}{space 4}   .04759{col 84}{space 3} .2151451
{txt}{space 20}GDP_dummy {c |}{col 31}{res}{space 2}-.1494106{col 43}{space 2} .0263919{col 54}{space 1}   -5.66{col 63}{space 3}0.000{col 71}{space 4}-.2011377{col 84}{space 3}-.0976835
{txt}{space 13}Investment_dummy {c |}{col 31}{res}{space 2}-.2161372{col 43}{space 2} .0272244{col 54}{space 1}   -7.94{col 63}{space 3}0.000{col 71}{space 4}-.2694961{col 84}{space 3}-.1627783
{txt}{space 17}Labour_dummy {c |}{col 31}{res}{space 2} .1047744{col 43}{space 2} .0399581{col 54}{space 1}    2.62{col 63}{space 3}0.009{col 71}{space 4}  .026458{col 84}{space 3} .1830908
{txt}{space 16}Inflows_dummy {c |}{col 31}{res}{space 2}-.2711904{col 43}{space 2} .0709313{col 54}{space 1}   -3.82{col 63}{space 3}0.000{col 71}{space 4}-.4102132{col 84}{space 3}-.1321677
{txt}{space 4}Balance_of_payments_dummy {c |}{col 31}{res}{space 2}-.1299142{col 43}{space 2} .0573706{col 54}{space 1}   -2.26{col 63}{space 3}0.024{col 71}{space 4}-.2423586{col 84}{space 3}-.0174698
{txt}{space 19}Debt_dummy {c |}{col 31}{res}{space 2} .3082498{col 43}{space 2} .1315991{col 54}{space 1}    2.34{col 63}{space 3}0.019{col 71}{space 4} .0503204{col 84}{space 3} .5661792
{txt}{space 8}World_indicator_dummy {c |}{col 31}{res}{space 2}-.1872307{col 43}{space 2} .1333518{col 54}{space 1}   -1.40{col 63}{space 3}0.160{col 71}{space 4}-.4485954{col 84}{space 3}  .074134
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 1.176577{col 43}{space 2} .3429738{col 54}{space 1}    3.43{col 63}{space 3}0.001{col 71}{space 4} .5043609{col 84}{space 3} 1.848793
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} .6600469{col 44} .1990026{col 58} .3655439{col 70} 1.191818
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.163306{col 44} .0536654{col 58} 1.062739{col 70}  1.27339
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}18.13{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. 
. 
. 
. **OVERALL Sample
. use "D:\jopr_yesilyurt_yesilyurt\overallsample.dta", clear
{txt}
{com}. *OVERALL Sample CLUSTER
. regress t Precision Intime_period  Innumber_of_countries Post_cold_war_dummy Panel_data_dummy Dynamic_model_dummy Heteroskedasticity_test_dummy Descriptive_statistics_dummy World_Bank_data_dummy SIPRI_data_dummy Developed_countries_dummy Developing_countries_dummy Europe_Countries_dummy OLS_dummy Dummy70 GDP_dummy Investment_dummy School_enrolment_dummy  Balance_of_payments_dummy Debt_dummy Money_dummy  World_indicator_dummy Non_military_expenditure_dummy,cluster(id)

{txt}Linear regression                               Number of obs     = {res}       554
                                                {txt}F(23, 90)         =  {res}   126.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5524
                                                {txt}Root MSE          =    {res}  1.659

{txt}{ralign 96:(Std. Err. adjusted for {res:91} clusters in id)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                             t{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      t{col 64}   P>|t|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}Precision {c |}{col 32}{res}{space 2} .4091017{col 44}{space 2}  .113851{col 55}{space 1}    3.59{col 64}{space 3}0.001{col 72}{space 4} .1829169{col 85}{space 3} .6352865
{txt}{space 17}Intime_period {c |}{col 32}{res}{space 2} -.363418{col 44}{space 2} .1859141{col 55}{space 1}   -1.95{col 64}{space 3}0.054{col 72}{space 4}-.7327689{col 85}{space 3} .0059328
{txt}{space 9}Innumber_of_countries {c |}{col 32}{res}{space 2}-.3494305{col 44}{space 2}   .17285{col 55}{space 1}   -2.02{col 64}{space 3}0.046{col 72}{space 4}-.6928272{col 85}{space 3}-.0060337
{txt}{space 11}Post_cold_war_dummy {c |}{col 32}{res}{space 2}-.2162823{col 44}{space 2} .0479903{col 55}{space 1}   -4.51{col 64}{space 3}0.000{col 72}{space 4}-.3116235{col 85}{space 3}-.1209411
{txt}{space 14}Panel_data_dummy {c |}{col 32}{res}{space 2}-.1345932{col 44}{space 2} .0446806{col 55}{space 1}   -3.01{col 64}{space 3}0.003{col 72}{space 4} -.223359{col 85}{space 3}-.0458274
{txt}{space 11}Dynamic_model_dummy {c |}{col 32}{res}{space 2}-.2003971{col 44}{space 2}  .037311{col 55}{space 1}   -5.37{col 64}{space 3}0.000{col 72}{space 4}-.2745219{col 85}{space 3}-.1262723
{txt}{space 1}Heteroskedasticity_test_dummy {c |}{col 32}{res}{space 2} .0948749{col 44}{space 2} .0511953{col 55}{space 1}    1.85{col 64}{space 3}0.067{col 72}{space 4}-.0068335{col 85}{space 3} .1965833
{txt}{space 2}Descriptive_statistics_dummy {c |}{col 32}{res}{space 2} .0934222{col 44}{space 2} .0261337{col 55}{space 1}    3.57{col 64}{space 3}0.001{col 72}{space 4} .0415029{col 85}{space 3} .1453414
{txt}{space 9}World_Bank_data_dummy {c |}{col 32}{res}{space 2} .1273084{col 44}{space 2} .0357742{col 55}{space 1}    3.56{col 64}{space 3}0.001{col 72}{space 4} .0562367{col 85}{space 3} .1983801
{txt}{space 14}SIPRI_data_dummy {c |}{col 32}{res}{space 2} .1430269{col 44}{space 2} .0373804{col 55}{space 1}    3.83{col 64}{space 3}0.000{col 72}{space 4} .0687642{col 85}{space 3} .2172896
{txt}{space 5}Developed_countries_dummy {c |}{col 32}{res}{space 2} .1062832{col 44}{space 2} .0395669{col 55}{space 1}    2.69{col 64}{space 3}0.009{col 72}{space 4} .0276767{col 85}{space 3} .1848897
{txt}{space 4}Developing_countries_dummy {c |}{col 32}{res}{space 2} .0524943{col 44}{space 2} .0278821{col 55}{space 1}    1.88{col 64}{space 3}0.063{col 72}{space 4}-.0028985{col 85}{space 3}  .107887
{txt}{space 8}Europe_Countries_dummy {c |}{col 32}{res}{space 2}-.1457767{col 44}{space 2} .0431516{col 55}{space 1}   -3.38{col 64}{space 3}0.001{col 72}{space 4}-.2315048{col 85}{space 3}-.0600485
{txt}{space 21}OLS_dummy {c |}{col 32}{res}{space 2}-.0335387{col 44}{space 2} .0151505{col 55}{space 1}   -2.21{col 64}{space 3}0.029{col 72}{space 4}-.0636377{col 85}{space 3}-.0034397
{txt}{space 23}Dummy70 {c |}{col 32}{res}{space 2}-.2033566{col 44}{space 2} .0603103{col 55}{space 1}   -3.37{col 64}{space 3}0.001{col 72}{space 4}-.3231735{col 85}{space 3}-.0835397
{txt}{space 21}GDP_dummy {c |}{col 32}{res}{space 2}-.1026986{col 44}{space 2} .0249319{col 55}{space 1}   -4.12{col 64}{space 3}0.000{col 72}{space 4}-.1522302{col 85}{space 3} -.053167
{txt}{space 14}Investment_dummy {c |}{col 32}{res}{space 2}-.1192398{col 44}{space 2} .0325814{col 55}{space 1}   -3.66{col 64}{space 3}0.000{col 72}{space 4}-.1839683{col 85}{space 3}-.0545112
{txt}{space 8}School_enrolment_dummy {c |}{col 32}{res}{space 2} .0726276{col 44}{space 2} .0345142{col 55}{space 1}    2.10{col 64}{space 3}0.038{col 72}{space 4} .0040591{col 85}{space 3} .1411962
{txt}{space 5}Balance_of_payments_dummy {c |}{col 32}{res}{space 2}-.1485816{col 44}{space 2} .0706574{col 55}{space 1}   -2.10{col 64}{space 3}0.038{col 72}{space 4}-.2889548{col 85}{space 3}-.0082085
{txt}{space 20}Debt_dummy {c |}{col 32}{res}{space 2} .3277363{col 44}{space 2} .1009553{col 55}{space 1}    3.25{col 64}{space 3}0.002{col 72}{space 4} .1271709{col 85}{space 3} .5283016
{txt}{space 19}Money_dummy {c |}{col 32}{res}{space 2} .4047559{col 44}{space 2} .0681283{col 55}{space 1}    5.94{col 64}{space 3}0.000{col 72}{space 4} .2694072{col 85}{space 3} .5401046
{txt}{space 9}World_indicator_dummy {c |}{col 32}{res}{space 2} -.379148{col 44}{space 2} .0798171{col 55}{space 1}   -4.75{col 64}{space 3}0.000{col 72}{space 4}-.5377187{col 85}{space 3}-.2205773
{txt}Non_military_expenditure_dummy {c |}{col 32}{res}{space 2} .1189711{col 44}{space 2} .0467238{col 55}{space 1}    2.55{col 64}{space 3}0.013{col 72}{space 4} .0261461{col 85}{space 3} .2117961
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 1.270621{col 44}{space 2} 1.087782{col 55}{space 1}    1.17{col 64}{space 3}0.246{col 72}{space 4}-.8904489{col 85}{space 3}  3.43169
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *OVERALL Sample hierarchical linear model OLS
. xtmixed t Precision Intime_period  Innumber_of_countries Post_cold_war_dummy Panel_data_dummy Dynamic_model_dummy Heteroskedasticity_test_dummy Descriptive_statistics_dummy World_Bank_data_dummy SIPRI_data_dummy Developed_countries_dummy Developing_countries_dummy Europe_Countries_dummy OLS_dummy Dummy70 GDP_dummy Investment_dummy School_enrolment_dummy  Balance_of_payments_dummy Debt_dummy Money_dummy  World_indicator_dummy Non_military_expenditure_dummy || id:
{res}
{txt}Performing EM optimization: 
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1015.6256}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-1015.6256}  
{res}
{txt}Computing standard errors:
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}       554
{txt}Group variable: {res}id{col 49}{txt}Number of groups{col 67}={col 69}{res}        91

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         1
{txt}{col 63}avg{col 67}={col 69}{res}       6.1
{txt}{col 63}max{col 67}={col 69}{res}        58

{col 49}{txt}Wald chi2({res}23{txt}){col 67}={col 70}{res}   296.67
{txt}Log likelihood = {res}-1015.6256{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                             t{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}Precision {c |}{col 32}{res}{space 2} .3777708{col 44}{space 2} .0692206{col 55}{space 1}    5.46{col 64}{space 3}0.000{col 72}{space 4} .2421009{col 85}{space 3} .5134407
{txt}{space 17}Intime_period {c |}{col 32}{res}{space 2}-.2514554{col 44}{space 2} .1357027{col 55}{space 1}   -1.85{col 64}{space 3}0.064{col 72}{space 4}-.5174278{col 85}{space 3} .0145169
{txt}{space 9}Innumber_of_countries {c |}{col 32}{res}{space 2}-.1176575{col 44}{space 2} .1170574{col 55}{space 1}   -1.01{col 64}{space 3}0.315{col 72}{space 4}-.3470858{col 85}{space 3} .1117709
{txt}{space 11}Post_cold_war_dummy {c |}{col 32}{res}{space 2}-.1560293{col 44}{space 2}  .038603{col 55}{space 1}   -4.04{col 64}{space 3}0.000{col 72}{space 4}-.2316897{col 85}{space 3}-.0803688
{txt}{space 14}Panel_data_dummy {c |}{col 32}{res}{space 2}-.1391383{col 44}{space 2} .0319137{col 55}{space 1}   -4.36{col 64}{space 3}0.000{col 72}{space 4}-.2016881{col 85}{space 3}-.0765886
{txt}{space 11}Dynamic_model_dummy {c |}{col 32}{res}{space 2}-.2407368{col 44}{space 2} .0320803{col 55}{space 1}   -7.50{col 64}{space 3}0.000{col 72}{space 4} -.303613{col 85}{space 3}-.1778607
{txt}{space 1}Heteroskedasticity_test_dummy {c |}{col 32}{res}{space 2} .1085593{col 44}{space 2} .0367103{col 55}{space 1}    2.96{col 64}{space 3}0.003{col 72}{space 4} .0366084{col 85}{space 3} .1805102
{txt}{space 2}Descriptive_statistics_dummy {c |}{col 32}{res}{space 2} .0862898{col 44}{space 2}  .029254{col 55}{space 1}    2.95{col 64}{space 3}0.003{col 72}{space 4}  .028953{col 85}{space 3} .1436266
{txt}{space 9}World_Bank_data_dummy {c |}{col 32}{res}{space 2} .0499007{col 44}{space 2} .0449612{col 55}{space 1}    1.11{col 64}{space 3}0.267{col 72}{space 4}-.0382216{col 85}{space 3} .1380231
{txt}{space 14}SIPRI_data_dummy {c |}{col 32}{res}{space 2} .1383679{col 44}{space 2} .0344424{col 55}{space 1}    4.02{col 64}{space 3}0.000{col 72}{space 4} .0708621{col 85}{space 3} .2058737
{txt}{space 5}Developed_countries_dummy {c |}{col 32}{res}{space 2} .0667049{col 44}{space 2} .0278515{col 55}{space 1}    2.40{col 64}{space 3}0.017{col 72}{space 4}  .012117{col 85}{space 3} .1212929
{txt}{space 4}Developing_countries_dummy {c |}{col 32}{res}{space 2} .0424923{col 44}{space 2} .0129988{col 55}{space 1}    3.27{col 64}{space 3}0.001{col 72}{space 4} .0170151{col 85}{space 3} .0679695
{txt}{space 8}Europe_Countries_dummy {c |}{col 32}{res}{space 2}-.0945091{col 44}{space 2} .0333668{col 55}{space 1}   -2.83{col 64}{space 3}0.005{col 72}{space 4}-.1599068{col 85}{space 3}-.0291113
{txt}{space 21}OLS_dummy {c |}{col 32}{res}{space 2}-.0362048{col 44}{space 2} .0169332{col 55}{space 1}   -2.14{col 64}{space 3}0.033{col 72}{space 4}-.0693932{col 85}{space 3}-.0030163
{txt}{space 23}Dummy70 {c |}{col 32}{res}{space 2}-.1761253{col 44}{space 2} .0374215{col 55}{space 1}   -4.71{col 64}{space 3}0.000{col 72}{space 4}-.2494702{col 85}{space 3}-.1027804
{txt}{space 21}GDP_dummy {c |}{col 32}{res}{space 2}-.1514835{col 44}{space 2} .0211616{col 55}{space 1}   -7.16{col 64}{space 3}0.000{col 72}{space 4}-.1929595{col 85}{space 3}-.1100075
{txt}{space 14}Investment_dummy {c |}{col 32}{res}{space 2}-.0875069{col 44}{space 2} .0279941{col 55}{space 1}   -3.13{col 64}{space 3}0.002{col 72}{space 4}-.1423743{col 85}{space 3}-.0326395
{txt}{space 8}School_enrolment_dummy {c |}{col 32}{res}{space 2} .0681695{col 44}{space 2} .0318604{col 55}{space 1}    2.14{col 64}{space 3}0.032{col 72}{space 4} .0057243{col 85}{space 3} .1306147
{txt}{space 5}Balance_of_payments_dummy {c |}{col 32}{res}{space 2}-.0765201{col 44}{space 2} .0575334{col 55}{space 1}   -1.33{col 64}{space 3}0.184{col 72}{space 4}-.1892835{col 85}{space 3} .0362433
{txt}{space 20}Debt_dummy {c |}{col 32}{res}{space 2} .3667291{col 44}{space 2} .0603581{col 55}{space 1}    6.08{col 64}{space 3}0.000{col 72}{space 4} .2484295{col 85}{space 3} .4850288
{txt}{space 19}Money_dummy {c |}{col 32}{res}{space 2} .4390001{col 44}{space 2}  .069959{col 55}{space 1}    6.28{col 64}{space 3}0.000{col 72}{space 4}  .301883{col 85}{space 3} .5761172
{txt}{space 9}World_indicator_dummy {c |}{col 32}{res}{space 2}-.3666694{col 44}{space 2} .0630196{col 55}{space 1}   -5.82{col 64}{space 3}0.000{col 72}{space 4}-.4901856{col 85}{space 3}-.2431532
{txt}Non_military_expenditure_dummy {c |}{col 32}{res}{space 2} .0552869{col 44}{space 2} .0497822{col 55}{space 1}    1.11{col 64}{space 3}0.267{col 72}{space 4}-.0422845{col 85}{space 3} .1528582
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} .0929513{col 44}{space 2} .7367891{col 55}{space 1}    0.13{col 64}{space 3}0.900{col 72}{space 4}-1.351129{col 85}{space 3} 1.537031
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}id{txt}: Identity{col 30}{c |}
{col 20}sd(_cons){col 30}{c |}{res}{col 33} 1.167335{col 44} .1478306{col 58}  .910751{col 70} 1.496205
{txt}{hline 29}{c +}{hline 48}
{col 17}sd(Residual){col 30}{c |}{res}{col 33} 1.353636{col 44} .0458485{col 58} 1.266692{col 70} 1.446547
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}77.26{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. 
{txt}end of do-file

{com}. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}D:\jopr_yesilyurt_yesilyurt\yesilyurt_yeslyurt.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 5 May 2019, 20:07:43
{txt}{.-}
{smcl}
{txt}{sf}{ul off}